Data tagging

ABSTRACT

A method for tagging and organizing data is provided. In one example, physiological data detected from a wearer of a wearable device is received and associated with a tag based, at least in art, on an input by the wearer. The input may be a state of the wearer, such as physical or mental state, or a rule. The collected physiological data may be organized based on the tag and, in some examples, on other types of received data, such as a wearer&#39;s personal data. In other example methods, data may be stored in a database based on one or more tags associated with the data.

BACKGROUND

Unless otherwise indicated herein, the materials described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

A number of scientific methods have been developed in the medical fieldto evaluate a person's health state. A person's health state may, forexample, be evaluated based on the measurement of one or morephysiological parameters, such as blood pressure, pulse rate, skintemperature, or galvanic skin response (GSR). In a typical scenario,these measurements may be taken in the home or a health-care setting byusing several discreet devices or sensors and, in some cases, by drawingblood or other bodily fluid. For most people, the measurements or bloodtests are performed infrequently, and changes in a physiologicalparameter, which may be relevant to health state, may not be identified,if at all, until the next measurement is performed.

In another example, these parameters may be more frequently orcontinuously measured, and other health-related information obtained, bya wearable device. The device, which may be provided as a wrist-mounteddevice, may include one or more sensors for detecting or measuring oneor more physiological parameters. For example, a wrist-mounted devicemay include optical sensors for heart rate and blood oxygen saturation(SpO₂) monitoring, a thermistor for measuring skin temperature, and aGSR sensor for measuring skin resistance. At least some of thephysiological parameter information may be obtained by detecting thepresence, absence and/or concentration of one or more analytes in thebody. The wearable device may further include or be in communicationwith other sensors such as accelerometers, inertial measurement units(IMU), infrared sensors, ultrasonic sensors, optical sensors,gyroscopes, magnetometers, odometers, pedometers, pressure sensors,strain gauges, GPS devices, a clock, etc.

Data collected by one or more wearable devices may be transmitted to thecloud or other remote server or device. Because each device may includeseveral sensors collecting data continuously, or at a relatively highrate, the amount of data transmitted to the cloud may be voluminous. Thetransmitted raw data, by itself, may also be difficult to search or use.

SUMMARY

A wearable device may collect physiological data from a wearer of thedevice and transmit that data to the cloud or other remote server ordevice. A tag may be associated with all or part of the data based, atleast in part, on an input by a wearer of the device. The input may bean indication of the state of the wearer, such as physical or mentalstate, or it may be a rule. Additional data associated with the wearermay also be synchronously collected by the system, such as personal(e.g., age, sex, occupation), motion (e.g., type of movement, speed,acceleration), and contextual (e.g., location, ambient temperature, timeof day) data, and associated with tag. The collected data may beorganized based on the tag and, in some examples, stored in a database.Data may also be collected from a population of wearers of the devices.

Some embodiments of the present disclosure provide a method including:(1) receiving, by a server from a wearable device, physiological datadetected by a wearable device, wherein the wearable device is configuredto be mounted to a body surface of a wearer; (2) receiving, by theserver, an input from the wearer; (3) associating, by the server, all orpart of the physiological data with a tag based, at least in part, onthe input from the wearer; and (4) organizing, by the server, thephysiological data based, at least in part, on the tag.

Further embodiments of the present disclosure provide a methodincluding: (1) receiving, by a server, physiological data detected by awearable device; (2) receiving, by the server, an input from a wearer ofthe wearable device; (3) associating, by the server, all or part of thephysiological data with a tag based, at least in part, on the input fromthe wearer of the wearable device; (5) establishing, by the server, oneor more groups based, at least in part, on the tag; and (5) storing, bythe server, the physiological data in a database to indicate that all orpart of the physiological data is a member of the one or more groups.

Still further embodiments of the present disclosure provide a methodincluding: (1) receiving, by a server, initial physiological data from awearable device; (2) receiving, by the server, an input from a wearer ofthe wearable device; (3) applying, by the server, a tag to all or aportion of the initial physiological data based on the input from thewearer of the wearable device; (4) receiving, by the server, subsequentphysiological data from the wearable device; and (5) determining, by theserver, whether to apply the tag to all or a portion of the subsequentdata.

These as well as other aspects, advantages, and alternatives, willbecome apparent to those of ordinary skill in the art by reading thefollowing detailed description, with reference where appropriate to theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example system that includes a wearabledevice, according to an example embodiment.

FIG. 2 illustrates an example of a wearable device.

FIG. 3 is a flow chart of an example method, according to an exampleembodiment.

FIG. 4 is a flow chart of an example method, according to an exampleembodiment.

FIG. 5 is a flow chart of an example method, according to an exampleembodiment.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying figures, which form a part hereof. In the figures, similarsymbols typically identify similar components, unless context dictatesotherwise. The illustrative embodiments described in the detaileddescription, figures, and claims are not meant to be limiting. Otherembodiments may be utilized, and other changes may be made, withoutdeparting from the scope of the subject matter presented herein. It willbe readily understood that the aspects of the present disclosure, asgenerally described herein, and illustrated in the figures, can bearranged, substituted, combined, separated, and designed in a widevariety of different configurations, all of which are explicitlycontemplated herein.

I. Overview

A wearable device may collect physiological and other data from a wearerof the device and transmit that data to the cloud or other remote serveror device. For example, the wearable device may detect one or morephysiological parameters, such as heart rate, blood pressure,respiration rate, blood oxygen saturation (SpO₂), skin temperature, skincolor, galvanic skin response (GSR), muscle movement, eye movement,blinking, and speech. Some physiological data may also be obtained bynon-invasively detecting and/or measuring one or more analytes presentin blood, saliva, tear fluid, or other body fluid of the wearer of thedevice. The one or more analytes could include enzymes, reagents,hormones, proteins, viruses, bacteria, cells or other molecules, such ascarbohydrates, e.g., glucose. Further, the wearable device, or a deviceassociated with the wearable device, may collect motion-related data,such as the wearer's speed of travel, altitude, acceleration, cadence ofmovement, intensity of movement, direction of travel, orientation,gravitational force, inertia, and rotation. This data may be collectedby sensors such as accelerometers, IMUs, proximity sensors, microphones,gyroscopes, magnetometers, optical sensors, ultrasonic sensors,odometers, and pedometers. Additionally, the wearable device may collectcertain contextual data, such as a wearer's location, ambient lightintensity, ambient temperature, time of day, a wearer's mode of travel,and a type of activity a wearer is participating in. Accordingly, thewearable device may include a location-tracking sensor (e.g., a GPSdevice), a light intensity sensor, a thermometer, and a clock. Awearer's personal or demographic data, such as sex, race, region orcountry of origin, age, weight, height, employment, medical history,etc., may also be collected and transmitted to the cloud.

The term “wearable device,” as used in this disclosure, refers to anydevice that is capable of being worn or mounted at, on, in or inproximity to a body surface, such as a wrist, ankle, waist, chest, ear,eye, head or other body part. As such, the wearable device can collectdata while in contact with or proximate to the body. For example, thewearable device can be configured to be part of a contact lens, awristwatch, a “head-mountable display” (HMD), an orally-mountable devicesuch as a retainer or orthodontic braces, a headband, a pair ofeyeglasses, jewelry (e.g., earrings, ring, bracelet), a head cover suchas a hat or cap, a belt, an earpiece, other clothing (e.g., a scarf),and/or other devices. Further, the wearable device may be mounteddirectly to a portion of the body with an adhesive substrate, forexample, in the form of a patch, or may be implanted in the body, suchas in the skin or another organ.

In some examples, the data described above may be collected directly bysensors integrated on the wearable device. Alternatively, oradditionally, some or all of the data described above may be collectedby sensors placed on other portions of a wearer's body or incommunication with the body, other computing devices remote to thewearable device (such as a remote device having location tracking andinternet capabilities, e.g. a smartphone, tablet or head-mountabledevice), or by manual input by the wearer. For example, the wearer maymanually input when she is exercising, eating, work, or sleeping, thetype of activity she is engaged in (running, typing, walking, climbing),her self-evaluated physical, health or mental state or mood (e.g.,hungry, tired, headache, anxious, etc.), among other things. Data mayalso be collected from applications on other computing devices linkedwith the wearable device such as an electronic calendar, social mediaapplications, restaurant reservation applications, travel applications,etc.

The wearable device (or remote device, the cloud, remote server, etc.)may be configured to automatically tag certain data collected from awearer based on preset rules. The rules may include certain thresholdlevels, or other identifiable characteristics of the data that are knownto be associated with the particular tag. The system would be configuredto look for data meeting those characteristics and automatically apply atag to that data. For example, the system may be configured toautomatically apply a “sleeping” tag when some data or combination ofdata streams, such as heart rate, respiration rate and eye movementdata, fall within certain ranges or exhibit certain characteristics.Additionally, the system may be configured to automatically apply a tagbased on rules set by the wearer of the device or by a physician orother third party. In some examples, the wearer may desire to setcertain goals, restrictions or thresholds on certain activities orhabits, such as daily caloric intake, number of hours spent sedentary,daily water intake, etc. Rules may be set in the system to automaticallytag data when goals are met or thresholds are exceeded.

The system may also be configured to receive an input from wearer of thedevice to tag a data point or segment of data. For example, the wearerof the device may indicate that she was sleeping from 10 PM to 6 AM andapply a “sleeping” tag to data collected during that time. The wearermay indicate a particular activity that she was previously engaged infor a certain period of time (e.g., exercising for 30 minutes), ispresently engaged in (e.g., “working until 5 PM”) or an activity thatshe anticipates being engaged in at a future time (e.g., “travelling byairplane from 8-11 AM”). The system may be configured to tag the datacollected during the relevant time periods with the activity engaged inby the wearer of the device. These activities may be broken down intomany categories and subcategories. For example, while “working” a wearercould also indicate whether he or she was sitting at her desk, at ameeting with a client or superior, taking on the phone, typing, etc. Asdescribed above, the system may also accept other inputs from the wearerabout his or her physical state or mood. Many other annotations arecontemplated.

Moreover, the wearable device, remote device, the cloud, or remoteserver may use supervised or machine learning to automatically determinewhen a tag should be applied to a data point or set of data. Once awearer of a device identifies a data point or set of data as beingassociated with a particular tag, the system may be configured to“learn” or recognize other data that could be associated with that tagwithout the need for the wearer's manual input. In a first instancewhere a wearer selects a tag to be applied to incoming data, thewearable device may be configured to take high resolution data duringthat time period to assist in the learning process.

Tags, such as labels or annotations, may be applied to individual datapoints, or to sets of data. For example, if a wearer inputs into thewearable device or connected computing device that she is hungry, a“snapshot” of all data collected at that instantaneous time may beassociated with the “hungry” tag. The system may be configured withcertain preset tags that may be chosen by a wearer, or automaticallyapplied to data. Such preset tags may make data aggregation moreconsistent and reliable and may include any category, metric orclassification that may be deemed useful or interesting to a personviewing the data. Additionally, or alternatively, the system may beconfigured to allow the wearer to formulate or choose original tags.

All of the data collected by or input into the wearable device and anyremote devices, and any tags applied thereto, may be time-synchronizedand sent to the cloud. For example, if a wearer indicates that she isrunning, all data synchronously collected by the wearable device or anyremote device should be tagged, for example, as “running” or “exercise”relevant data. The usability and searchability of the data collected andstored in the cloud may be increased by data tagging. Tags may be usedby those viewing the data, such as a wearer, clinicians, physicians ormarketing firms, to easily search for and collect relevant data points.In addition, tagging may assist in prioritizing analysis of data.

In particular, tags may make it possible to access and process largeamounts of data quickly and easily, without having to search through adatabase for a particular type of data. Data may be organized based onthe tags, such as, by aggregating all data having a common tag, e.g. theaverage number of hours all wearers slept in the past week. Tags may beused to recognize correlations or identify causation between data andthe category of the tag. These correlations may be for an individualwearer or for a population of wearers and may be used to diagnose apresent medical condition in a wearer of the device, or to predict thepossible occurrence of a medical condition in the future. Tags may alsobe used by the system to assist a wearer in viewing, organizing orunderstanding her own data. For example, the tags may be used to computeperformance or health statistics, such as the number of days the wearerjogged per week, and may assist a wearer in assessing set goals, such asthe number of days she stayed below her caloric intake goal. Tags mayalso enable a wearer to compare her data to that of others having acommon or similar tag. The tags may also be used to make recommendationsto wearers. For example, processors in the cloud or remote server mayanalyze the data and identify trends, correlations, patterns, milestoneevents, etc., and recommend certain actions, products, remedies, etc. tothe wearer of the device.

The term “medical condition” as used herein should be understood broadlyto include any disease, illness, disorder, injury, condition orimpairment—e.g., physiologic, psychological, cardiac, vascular,orthopedic, visual, speech, or hearing—or any situation affecting thehealth of the wearer of the device or requiring medical attention. A“medical condition” may also include a situation where a physiologicalparameter falls outside of a range, regimen or recommendation set by anindividual, her physician, a clinician or a nutritionist. For example, a“medical condition” may be indicated when an individual consumes morethan the daily recommended calories or consumes food having a high levelof fat or sugars.

It should be understood that the above embodiments, and otherembodiments described herein, are provided for explanatory purposes, andare not intended to be limiting.

II. Example Wearable Devices and Systems

A system 100, including one or more wearable devices 200 configured tobe mounted to or worn on, in or in proximity to a body 10, one or moreremote sensors 120, and one or more computing devices 130 all incommunication with a server 140, is shown in FIG. 1. Remote sensor 120may be any sensor not provided directly on the wearable device 200. Forexample, a remote sensor 120 may be mounted to a wearer's bicycle orcar, on the wearer's desk, near a wearer's bed or outside of a wearer'shome. Computing device 130 may be any device having computing orinternet capabilities, including a smartphone or tablet, a personalcomputer, a mobile or cellular telephone, or a web-based application. Inone embodiment, the one or more remote sensors 120 and wearable devices200 indirectly communicate with server 140 via computing device 130. Inother embodiments, the wearable device 200, remote sensor 120 andcomputing device 130 may all directly communicate with the server 140.

The device 200, remote sensor 120 and/or computing device 130 may becapable of collecting, detecting or measuring a plurality of parametersfrom or associated with a person wearing the device, such asphysiological, motion, contextual, and personal parameters. As will bedescribed further below, these parameters may be detected on one or moreof the wearable device 200, the remote sensors 120 and the computingdevices 130. Physiological parameters may include heart rate, bloodpressure, respiration rate, blood oxygen saturation (SpO₂), skintemperature, skin color, galvanic skin response (GSR), perspiration,muscle movement, eye movement, blinking, speech and analyteconcentration. Motion-related parameters, such as the wearer's speed oftravel, altitude, acceleration, cadence of movement, intensity ofmovement, direction of travel, orientation, gravitational force,inertia, and rotation. Contextual parameters, such as a wearer'slocation, ambient light intensity, ambient temperature, humidity,allergen levels, pollution, time of day, a wearer's mode of travel, anda type of activity a wearer is participating in, may also be collected.A wearer's “location” could be any location with respect to a2-dimensional or 3-dimensional coordinate system (e.g., a location withrespect to X, Y and Z axes) or with respect to a cartographic locationdescription (e.g., a street address), and may further include a globalposition (e.g., latitude, longitude and elevation), a hyper-localposition (such as a location within a home or building), and/or anyposition at any level of resolution therebetween. Personal parametersmay include sex, race, region or country of origin, age, weight, height,employment, occupation, and medical history, etc.

The wearable device 200, remote sensor(s) 120 and computing device(s)130 may be configured to transmit data, such as collected physiological,motion, contextual and personal parameter data via a communicationinterface over one or more communication networks to the remote server140. The communication interface may include any means for the transferof data, including both wired and wireless communications, such as auniversal serial bus (USB) interface, a secure digital (SD) cardinterface, a plain old telephone service (POTS) network, a cellularnetwork, a fiber network and a data network. In one embodiment, thecommunication interface includes a wireless transceiver for sending andreceiving communications to and from the server. The wearable device200, remote sensor(s) 120 and computing device(s) 130 may also beconfigured to communicate with one another via any communication means.

Further, the computing device 130 may be capable of accessinginformation on the internet, a wearer's electronic calendar, or from asoftware application. The computing device 130 may collect dataregarding the wearer's schedule, appointments, and planned travel. Insome cases, the computing device 130 may also access the internet orother software applications, such as those operating on a wearer'ssmartphone. For example, the computing device 130 may access anapplication to determine the temperature, weather and environmentalconditions at the wearer's location. Moreover, the computing device 130may access a wearer's social media applications, such as Facebook,Foursquare or Twitter, to determine restaurants, stores or otherlocations a wearer has visited. This data may, for example, be relevantto correlating a wearer's reported illness with a restaurant she ate at.All of this collected data may be transmitted to the remote server 140.

In addition to receiving data from the wearable device 200, remotesensor(s) 120 and computing device(s) 130, the server may also beconfigured to gather and/or receive additional information from othersources. For example, the server may be configured to regularly receiveviral illness or food poisoning outbreak data from the Centers forDisease Control (CDC) and weather, pollution and allergen data from theNational Weather Service. Further, the server may be configured toreceive data regarding a wearer's health state or existing medicalconditions from a hospital or physician. Such information may be used inthe server's decision-making process, such as recognizing correlationsand in generating recommendations.

One or more of the wearable device 200, remote sensor 120 or computingdevice 130 may also be capable of receiving an input from a wearer andtransmitting that input to the server 140. For example, the wearer mayinput one or more rules or an indication of her “state.” As will bedescribed further below, the wearable device 200 may include aninterface 280 with one or more controls 284 via which the wearer mayprovide an input. A wearer may also provide an input on a computingdevice 130, such as a smartphone, tablet or laptop computer.

Turning to FIG. 2, the wearable device 200 may be provided as any deviceconfigured to be mounted in, on or adjacent to a body surface. In theexample shown in FIG. 2, the wearable device 200 is a wrist-mountabledevice 210, but many other forms are contemplated. The device may beplaced in close proximity to the skin or tissue, but need not betouching or in intimate contact therewith. A mount 220, such as a belt,wristband, ankle band, necklace, or adhesive substrate, etc. can beprovided to mount the device at, on or in proximity to the body surface.

The wearable device 200 may include one or more sensors 230 forcollecting data from or associated with a wearer of the device 210, atransceiver 240 for transmitting collected data to a remote server ordevice, a processor 250 and a memory 260. Transceiver 240 may include awireless transceiver with an antenna that is capable of sending andreceiving information to and from a remote source, such as a server 140.Memory 260 is a non-transitory computer-readable medium that caninclude, without limitation, magnetic disks, optical disks, organicmemory, and/or any other volatile (e.g. RAM) or non-volatile (e.g. ROM)storage system readable by the processor 250. The memory 260 can includea data storage to store indications of data, such as sensor readings,program settings (e.g., to adjust behavior of the wearable device 200),user inputs (e.g., from a user interface on the device 200 orcommunicated from a remote device), etc. The memory 260 can also includeprogram instructions for execution by the processor 250 to cause thedevice 200 to perform processes specified by the instructions. Exampleprocessor(s) 250 include, but are not limited to, CPUs, GraphicsProcessing Units (GPUs), digital signal processors (DSPs), applicationspecific integrated circuits (ASICs).

The sensors 230 may include any device for collecting, detecting ormeasuring one or more physiological, motion, contextual or personalparameters. Sensors for detecting and measuring physiological parametersmay include, but are not limited to, one or more of an optical (e.g.,CMOS, CCD, photodiode), acoustic (e.g., piezoelectric, piezoceramic),electrochemical (voltage, impedance), resistive, thermal, mechanical(e.g., pressure, strain), magnetic, or electromagnetic (e.g., magneticresonance) sensor. In particular, the wearable device 100 may includesensors such as a thermometer and a GSR sensor for sensing temperatureand skin resistance, respectively, and a light emitting source and adetector for sensing blood pressure. Some physiological data may also beobtained by non-invasively detecting and/or measuring one or moreanalytes present in blood, saliva, tear fluid, or other body fluid ofthe wearer of the device. The one or more analytes could includeenzymes, reagents, hormones, proteins, viruses, bacteria, cells or othermolecules, such as carbohydrates, e.g., glucose. Analyte detection andmeasurement may be enabled through several possible mechanisms,including electrochemical reactions, change in impedance, voltage, orcurrent etc. across a working electrode, and/or interaction with atargeted bioreceptor. For example, analytes in a body fluid may bedetected or measured with one or more electrochemical sensors configuredto cause an analyte to undergo an electrochemical reaction (e.g., areduction and/or oxidation reaction) at a working electrode, one or morebiosensors configured to detect an interaction of the target analytewith a bioreceptor sensitive to that analyte (such as proteins, enzymes,reagents, nucleic acids, phages, lectins, antibodies, aptamers, etc.),and one or more impedimetric biosensors configured to measure analyteconcentrations at the surface of an electrode sensor by measuring changein impedance across the electrode, etc. Other detection andquantification systems and schemes are contemplated for implementationof the analyte sensor system.

Contextual parameters may be detected from, for example, alocation-tracking sensor (e.g., a GPS or other positioning device), alight intensity sensor, a thermometer, a microphone and a clock. Motiondata may be collected by sensors such as accelerometers, IMUs, proximitysensors, microphones, gyroscopes, magnetometers, optical sensors,ultrasonic sensors, odometers, and pedometers. These sensors and theircomponents may be miniaturized so that the wearable device may be wornon the body without significantly interfering with the wearer's usualactivities. Additionally or alternatively, these sensors may be providedon or as part of a remote sensor 120 or a computing device 130.

The wearable device 200 may also include an interface 280 via which thewearer of the device may receive one or more recommendations or alertsgenerated either from a remote server 140, remote computing device 130,or from the processor 250 provided on the device. The alerts could beany indication that can be noticed by the person wearing the wearabledevice. For example, the alert could include a visual component (e.g.,textual or graphical information on a display), an auditory component(e.g., an alarm sound), and/or tactile component (e.g., a vibration).Further, the interface 280 may include a display 282 where a visualindication of the alert or recommendation may be displayed. The display282 may further be configured to provide an indication of the detectedor collected physiological, motion, contextual or personal parameters,for instance, the wearer's heart rate. In embodiments where the wearabledevice is not capable of supporting an interface 280, alerts andrecommendations may be provided to the wearer on computing device 130.The interface 280 may also include one or more controls 284 via which auser may input an indication of her state, or, in some cases a rulerelated to the data detected by the wearable device.

In other examples, the wearable device 200 may be provided as or includean eye-mountable device, a head mountable device (HMD) or anorally-mountable device. An eye-mountable device may, in some examples,take the form of a vision correction and/or cosmetic contact lens,having a concave surface suitable to fit over a corneal surface of aneye and an opposing convex surface that does not interfere with eyelidmotion while the device is mounted to the eye. The eye-mountable devicemay include at least one sensor provided on a surface of or embedded inthe lens material for collecting data. In one example, the sensor can bean amperometric electrochemical sensor for sensing one or more analytespresent in tear fluid.

An HMD may generally be any display device that is capable of being wornon the head and places a display in front of one or both eyes of thewearer. Such displays may occupy a wearer's entire field of view, oroccupy only a portion of a wearer's field of view. Further, head-mounteddisplays may vary in size, taking a smaller form such as a glasses-styledisplay or a larger form such as a helmet or eyeglasses, for example.The HMD may include one or more sensors positioned thereon that maycontact or be in close proximity to the body of the wearer. The sensormay include a gyroscope, an accelerometer, a magnetometer, a lightsensor, an infrared sensor, and/or a microphone for collecting data fromor associated with a wearer. Other sensing devices may be included inaddition or in the alternative to the sensors that are specificallyidentified herein.

An orally mountable device may be any device that is capable of beingmounted, affixed, implanted or otherwise worn in the mouth, such as on,in or in proximity to a tooth, the tongue, a cheek, the palate, thelips, the upper or lower jaw, the gums, or other surface in the mouth.For example, the device 200 can be realized in a plurality of formsincluding, but not limited to, a crown, a retainer, dentures,orthodontic braces, dental implant, intra-tooth device, veneer,intradental device, mucosal implant, sublingual implant, gingivaeimplant, frenulum implant, or the like. The orally-mountable device mayinclude one or more sensors to detect and/or measure analyteconcentrations in substances in the mouth, including food, drink andsaliva. Sensor(s) that measure light, temperature, blood pressure, pulserate, respiration rate, air flow, and/or physiological parameters otherthan analyte concentration(s) can also be included.

Some embodiments of the system 100 and/or wearable devices 200 mayinclude privacy controls which may be automatically implemented orcontrolled by the wearer of the device. For example, where a wearer'scollected physiological parameter data and health state data areuploaded to a cloud computing network for trend analysis by a clinician,the data may be treated in one or more ways before it is stored or used,so that personally identifiable information is removed. For example, awearer's identity may be treated so that no personally identifiableinformation can be determined for the wearer, or a wearer's geographiclocation may be generalized where location information is obtained (suchas to a city, ZIP code, or state level), so that a particular locationof a wearer cannot be determined.

Additionally or alternatively, wearers of a device may be provided withan opportunity to control whether or how the device collects informationabout the wearer (e.g., information about a wearer's medical history,social actions or activities, profession, a wearer's preferences, or awearer's current location), or to control how such information may beused. Thus, the wearer may have control over how information iscollected about him or her and used by a clinician or physician or otherwearer of the data. For example, a wearer may elect that data, such ashealth state and physiological parameters, collected from his or herdevice may only be shared with certain parties or used in certain ways.

III. Example Methods

FIG. 3 is a flowchart of a method 300 for tagging data detected from awearer of a wearable device. As described above, a wearable device 200,configured to be mounted to a body surface, may detect physiologicaldata from a wearer of the device. This detected data may then bereceived by, for example, a processor on the wearable device 200, aremote computing device 130 or a remote server 140, such as a cloudcomputing network (310). An input by the wearer of the device may alsobe received, for example, by the server 140 (320). The input may bereceived directly by the server 140, or may be input by the wearer intothe wearable device 200, a remote sensor 120, or a computing device 130,such as a smartphone or laptop computer and transmitted to the server140. While embodiments of the foregoing methods are described herein asbeing carried out on the server 140, it is contemplated that the methodsmay be carried out by a processor on the wearable device 200, remotesensor 120 or computing device 130.

In one example, the wearer may input her “state”, which may include anytype of activity the wearer is engaged in, a task being performed by thewearer, and the wearer's a health state, physical state, mental stateand mood. For example, the wearer may indicate that she is at work,standing, sleeping, making dinner, or exercising. A wearer may alsoinput how she is feeling or any symptoms he or she is experiencing, suchas, “feeling cold,” “feeling tired,” “stressed,” “feeling rested andenergetic,” “hard to breathe,” etc. More than one state may also beinput at a time. In some examples, the wearer may concurrently input astate and one or more specific sub-states, such as, “at work” and“typing” or “at the gym” and “lifting weights.” In other examples, thewearer may concurrently input different types of states, such as, “atwork” and “feeling tired.” Further, the wearer may input that the stateapplies to a period of time, for example, a wearer may input that shewas at work between 9 am and 5 pm, or that she was running from 6 am to6:45 am. The system may also be configured with a stopwatch or timerfunction where a wearer may input when a state is initiated and inputwhen the state ends.

In another example, the input may be a rule set by the wearer of thedevice. The wearer may input rules against which future data may becompared. For example, the wearer may specify that she swims for 30minutes at the gym every Tuesday starting at 12 pm or that she is atwork between 9 am and 5 pm every weekday. The rule may be based in someembodiments on a threshold, such as, a maximum or minimum recommendedcaloric intake or a heart rate range for aerobic exercise. The rule mayalso be related to a goal of the wearer of the wearable device, such asa number of times the wearer aims to run in a week or the number ofhours of sleep a wearer aims to sleep each night.

Based on the wearer's input, all or part of the physiological data maybe associated with a tag by the server 140 (330). The tag may be anyword, phrase, expression, or symbol that can be used to label all orpart of the physiological data. In some examples, the wearer's input maybe used, at least in part, to generate the tag. For example, the wearermay input that she was sleeping between 10 pm and 6 am and a “sleeping”tag may be generated and applied to data collected between 10 pm and 6am. The generated tag may then be saved in the system and selected bythe wearer on another occasion. Tags may be associated withphysiological data synchronously with its collection, or they may beapplied at some time after data has been collected. If the wearer inputsthat she is beginning a run, then all data collected from that point intime until the wearer inputs that her run has ended will be associatedwith, for example, a “running” tag. Alternatively, the wearer may inputafter completion of an activity that, for example, she was sitting forthe last thirty minutes or that she experienced a headache yesterday atnoon.

In other examples, where the wearer's input is a rule, all or part ofthe collected physiological data may be associated with a tag based onthe rule. According to one example described above, where the wearerinputs a rule that she swims for 30 minutes at the gym every Tuesdaystarting at 12 pm, a “swimming” tag may be applied to all data collectedbetween 12 pm and 12:30 pm every Tuesday. Where, for example, the ruleis based on a threshold, a tag may be applied to collected data thatfalls above or below that threshold, or outside of a range. Further,where the rule is based on a goal set by the wearer, collected data maybe compared to the rule and tagged, for example, as “goal met,” “goalexceeded,” or “goal not met.”

The physiological data may be organized by the server based, at least inpart, on the tag associated with the collected data (340). In oneexample, the physiological data may be organized by aggregating aplurality of physiological data detected from the wearer of a wearabledevice associated with that tag. The data may be organized byaggregating all of a wearer's data that is associated with a “sleeping”tag. Similarly, data may be organized by aggregating all data that isassociated with two tags, such as, “consumed bread” and “stomach ache.”Further, data associated with a tag may be organized by time of day, dayof the week, and value. For example, data tagged with “sleeping” may beorganized by the number of hours of sleep or the time of day sleepstarted or ended. Data may also be organized by related tags, forexample all data tagged with “sleeping” may be organized with datatagged with “snoring.” Many other organization schemes are contemplated.

In some embodiments, tags may also be applied to data based on learnedrules. FIG. 4 illustrates a flow chart for an additional method 400.Initial physiological data is received by the server from a wearabledevice (410). The server also receives an input from the wearer of thewearable device (420) and applies a tag to all or a portion of thatinitial data based on the input received from the wearer (430).Subsequent physiological data is received by the server from thewearable device (440) and the server, or other computing device,determines whether to apply the tag to all or a portion of thesubsequent data (450). In some examples, the subsequent data may beautomatically associated with a tag based on a learned rule which may bebased, at least in part, on a comparison between some physiological datais associated with a tag and subsequently received physiological data.The server 140 or other computing device in the system 100 may beconfigured to use supervised or machine learning to determine when a tagshould be applied to a data point or set of data based on the type ofdata a particular tag was previously associated with. Once a data pointor set of data is associated with a particular tag based on a wearer'sinitial input, the system may be configured to “learn” or recognizeother data that could be associated with that tag without the need for asubsequent input by the wearer. The system may recognize patterns andgenerate rules based on these patterns. For example, the system mayrecognize that on a certain number of instances where a wearer inputthat she was sleeping, the wearer's heart rate and respiration ratealways fell within a certain range for that period of time. Accordingly,when subsequent heart rate and respiration data within that identifiedrange is received, the system may automatically apply a “sleeping” tagto that data without the wearer having to input that she was sleeping.This machine or supervised learning may be facilitated by taking highresolution data during a time period where a wearer inputs a tag for thefirst time.

Learned rules may also be based, at least in part, on an input by thewearer of the device. In some cases, a wearer may provide an input intothe system to “correct” a learned rule. For example, the system maycreate a learned rule such that, whenever a wearer's data exhibitsvalues A, B and C, then the data is associated with an “eating” tag. Asdescribed above, this learned rule may be based on an earlier inputreceived by the wearer indicating that she was eating at a time when hercollected data exhibited values A, B and C. The user may provide aninput at a later that may correct the tag by (1) change the initialindication upon which the learned rule was based from, for example,“eating” to “drinking” or (2) by correcting certain instances of whenthe learned rule applied the “eating” tag. For example, the wearer mayrecognize that the learned rule incorrectly applied to the “eating” tagto data collected while she was, in fact, drinking. In some examples,the system may use this further input by the user to adjust the learnedrule to distinguish between data collected during eating and datacollected during drinking.

Further, physiological data associated with a plurality of wearers ofrespective wearable devices may be received by the server and associatedwith one or more tags. The tags may be associated with each wearer'sdata based, at least in part, on an input from that respective wearerreceived by the server. The data collected from this plurality ofwearers may be organized by the server based on the one or more tagsapplied to their respective data. In some examples, the physiologicaldata may be organized by aggregating physiological data associated witheach of the plurality of wearers. Similar to that described above, datafrom any of the plurality of wearers that is associated with aparticular tag may be aggregated together.

Physiological data collected from a plurality of wearers may also beused in the creation of learned rules. The system may be configured to“learn” or recognize data collected from a wearer of the device thatshould be associated with a tag based on a comparison of that data todata collected from a population of wearers that is also associated withthat tag. Accordingly, a learned rule for applying a tag to a wearer'sdata may also be based on a comparison of that wearer's physiologicaldata to data collected from a plurality of wearers that is alsoassociated with that tag. In some cases, comparing a wearer's data todata collected from a plurality of wearers may improve the accuracy ofthese learned rules. For example, the system may be more able todetermine when a wearer is engaging in a particular activity (e.g.,bungee jumping) when it can compare the wearer's data to a plurality ofother data sets that are tagged with that activity.

In addition to physiological data, the server 140 may be configured toreceive motion data, contextual data, and personal data associated withthe wearer of a wearable device. As described above, these types of datamay be detected or collected by one or more of a wearable device 200, acomputing device 130, a remote sensor 120 or the server 140. The motionand contextual data may be detected synchronously with the physiologicaldata. Time synchronization data may also be received by the server 140that indicates a timing relationship between the physiological and themotion and/or contextual data. By time synchronizing the data, the sametag may be applied to all physiological, motion and contextual data thatis detected at the same time. A wearer's personal data may also beassociated with the other forms of data and may, in some examples, beused with the tag to organize the data. Data collected from a populationof wearers may be organized based on a common tag and one or morepersonal data parameters, such as height, weight, gender, sex, homeaddress, occupation, etc. For example, data collected from women betweenthe ages of 30 and 34 and tagged with “running” may be organizedtogether.

Another method 500 is illustrated in the flowchart of FIG. 5.Physiological data detected by a wearable device is received by a server(510) along with an input from the wearer of the wearable device (520).All or part of the physiological data is associated by the server with atag based, at least in part, on the input from a wearer of the wearabledevice (530), similar to that described above with respect to method300. Based on the tag, one or more groups are established by the server(540) and the physiological data is stored by the server in a databaseto indicate that all or part of the physiological data is a member ofthe one or more groups (550). In addition to being based on the tag, theone or more groups established in the database may also be based onpersonal data of a wearer of the device. For example, “running” groupmay be established within the database for women over the age of 50 andall data with a common tag and having those personal datacharacteristics would be stored within the database to indicate that thephysiological data associated with that tag was a member of the group.Further, a wearer's time-synchronized contextual and motion data mayalso be stored in the database with the corresponding physiological datato indicate that all or part of the contextual or motion data is amember of the group.

Tags may be used to organize data stored in the server 140 to increaseits usability and searchability. Tags may be used by the wearer inviewing and using her data or by third parties who have been givenpermission by the wearer, such as clinicians or physicians. In oneexample, tags may provide a convenient means to query a database inwhich a wearer or population of wearers' data is stored or toconveniently view large amounts of data in an organized fashion. Tagsmay be used aggregate similar data points or data sets from a pluralityof wearers or from a single wearer over time. The tags may also providea means for organizing many different types of data, such asphysiological, contextual, motion and personal data. To this end, datamay be aggregated based on one or more tags and one or more differenttypes of data. In addition, tagging may assist in prioritizing analysisof data, for example, all data tagged as being above a threshold may bereviewed first.

Tags may be used to recognize correlations or identify causation betweendata and the category of the tag. These correlations may be for anindividual wearer or for a population of wearers and may be used todiagnose a present medical condition in a wearer of the device, or topredict the possible occurrence of a medical condition in the future. Inaddition, the tags may be used to see change or stability of data overtime, for example, how a wearer's average resting heart rate changedover a three month period. Tags may be used to determine the effect thata tagged activity had on physiological data, such as the effect runninghad on a wearer's heart rate.

Correlations may be derived between physiological data measured awearable device and a tag, which may have been generated by the healthstate input by the wearer, and all other information collected by theserver or other computing device also associated with that tag. Forexample, analysis of physiological data associated with a tag (which mayrepresent a wearer's input health state) may reveal that the patientreported experiencing certain adverse health conditions, such as amigraine or a heart attack, when one or more physiological parametersreach a certain level. This correlation data may be used to generaterecommendations for the patient. Physiological data such as bloodpressure, heart rate, body temperature etc., may be complemented bymotion and contextual data, in order to add to or enhance thesecorrelations. In this respect, tags may be particularly useful indrawing correlations between many different types of data sharing acommon or similar tag. For example, the analysis of physiological datameasured by the wearable device 200 and the location of the wearabledevice (which may, for example, be determined from a remote sensor 120)may reveal that the wearer of the device experiences certain adversehealth conditions, such as allergic reaction, when present in a certaingeographical region. Further, date, time of day and geographicallocation data may be used to detect and monitor spatial and temporalspreading of diseases among a population of wearers.

Further, tags may be used by a wearer to view and use her own data. Forexample, the tags may be used to compute performance statistics, such asthe number of days the wearer jogged per week, or health statistics,such as the number of days the wearer spent on bed rest in the previousyear. The tags may also be used by a wearer of the device to determineif she met certain set goals, such as the number of days the wearerstayed below her caloric intake goal. Tags may also be used by thewearer of a device to run any number of queries on her data, e.g.,number of days experiencing hunger, number of days experiencing hungerand headache, nights REM sleep was achieved, etc. Further, the tags maybe used by a wearer to compare her data to that of others. For example,the wearer may determine how her exercise level compares to that ofothers in her sex, age group, location, profession, etc.

The tags may also be used to make recommendations to wearers. Forexample, processors in the remote server 140 may analyze the data andidentify trends, correlations, patterns, milestone events, etc., andrecommend certain actions, products, remedies, etc. to the wearer of thedevice. For example, the server may recognize that data collected from awearer on three different instances was tagged with “stomach ache”within a few hours after the wearer input that she consumed awheat-based product. The server may then recommend that the wearer ofthe device see a doctor to discuss a potential gluten allergy. Inanother example, the server may recommend that a wearer exercise more ifshe did not engage in the recommended level of activity during a certainperiod. These recommendations may be based on, for example, generallyrecognized standards or norms, on approved drug indications, or onintended or recommended product uses. The recommendations may also beset or provided by a wearer's physician or caretaker. In other examples,the tags may be used for targeted marketing. In one example, the systemmay recommend a product or service based on one or more tags applied toa wearer's data, such as, a recommendation for a pain medication aid ifthe wearer tagged data indicating she experienced a headache.

It will be readily understood that the aspects of the presentdisclosure, as generally described herein, and illustrated in thefigures, can be arranged, substituted, combined, separated, and designedin a wide variety of different configurations, all of which areexplicitly contemplated herein. While various aspects and embodimentshave been disclosed herein, other aspects and embodiments will beapparent to those skilled in the art.

Example methods and systems are described above. It should be understoodthat the words “example” and “exemplary” are used herein to mean“serving as an example, instance, or illustration.” Any embodiment orfeature described herein as being an “example” or “exemplary” is notnecessarily to be construed as preferred or advantageous over otherembodiments or features. Reference is made herein to the accompanyingfigures, which form a part thereof. In the figures, similar symbolstypically identify similar components, unless context dictatesotherwise. Other embodiments may be utilized, and other changes may bemade, without departing from the spirit or scope of the subject matterpresented herein. The various aspects and embodiments disclosed hereinare for purposes of illustration and are not intended to be limiting,with the true scope and spirit being indicated by the following claims.

What is claimed is:
 1. A method, comprising: receiving, by a server froma wearable device, first physiological data detected by a wearabledevice, wherein the wearable device is configured to be mounted to abody surface of a wearer; receiving, by the server, an input from thewearer; associating, by the server, all or part of the firstphysiological data with a tag based, at least in part, on the input fromthe wearer; organizing, by the server, the first physiological databased, at least in part, on the tag; receiving, by the server from oneor more other wearable devices, second physiological data related to oneor more wearers of the one or more other wearable devices, wherein thesecond physiological data is associated with the tag; receiving, by theserver from the wearable device, third physiological data detected bythe wearable device; determining, by the server, a learned rule based,at least in part, on (i) a comparison of at least some of the thirdphysiological data and the first physiological data associated with thetag and (ii) a comparison of at least some of the third physiologicaldata and the second physiological data associated with the tag; andassociating, by the server, all or part of the third physiological datawith the tag based on the learned rule.
 2. The method of claim 1,wherein the tag is generated based, at least in part, on the input fromthe wearer.
 3. The method of claim 1, wherein the input is a state ofthe wearer.
 4. The method of claim 3, wherein the state comprises one ormore of: (a) a type of activity engaged in by the wearer, (b) a taskperformed by the wearer, (c) a health state of the wearer, (d) aphysical state of the wearer, (e) a mental state of the wearer, or (f) amood of the wearer.
 5. The method of claim 1, wherein the input is arule.
 6. The method of claim 5, wherein the rule is based, at least inpart, on a threshold value.
 7. The method of claim 1, wherein thelearned rule is further based, at least in part, on the input by thewearer.
 8. The method of claim 1, wherein organizing the firstphysiological data comprises aggregating a plurality of physiologicaldata detected from the wearer of the wearable device associated with thetag.
 9. The method of claim 1, wherein the first physiological datacomprises one or more of: (a) a heart rate, (b) a respiration rate, (c)a body temperature, or (d) a level of perspiration.
 10. The method ofclaim 1, further comprising: associating, by the server, all or part ofthe second physiological data related to the one or more wearers of theone or more other wearable devices with the tag based, at least in part,on respective inputs from the one or more wearers; and organizing, bythe server, the second physiological data based, at least in part, onthe tag.
 11. The method of claim 10, wherein organizing the secondphysiological data comprises aggregating the second physiological datarelated to the one or more wearers based on the tag.
 12. The method ofclaim 1, further comprising: receiving, by the server, motion dataassociated with the wearer of the wearable device, wherein the motiondata is detected synchronously with the first physiological data;receiving, by the server, time-synchronization data that indicates atiming relationship between the motion data and the first physiologicaldata; and applying, by the server, the tag to the motion data based, atleast in part, on the time-synchronization data.
 13. The method of claim12, wherein the motion data comprises one or more of: (a) a speed oftravel, (b) an altitude, (c) an acceleration, (d) a cadence of movement,(e) an intensity of movement, (f) a direction of travel, (g) anorientation, (h) a gravitational force, (i) an inertia, or (j) arotation.
 14. The method of claim 1, further comprising: receiving, bythe server, contextual data associated with the wearer of the wearabledevice, wherein the contextual data is detected synchronously with thefirst physiological data; receiving, by the server, time-synchronizationdata that indicates a timing relationship between the contextual dataand the first physiological data; and applying, by the server, the tagto the contextual data based, at least in part, on thetime-synchronization data.
 15. The method of claim 14, wherein thecontextual data comprises one or more of: (a) a location of the wearerof the device, (b) an ambient light intensity, (c) an ambienttemperature, (c) a time of day, (d) a mode of travel of the wearer ofthe device, or (e) a type of activity the wearer of the device isengaged in.
 16. The method of claim 1, further comprising: receiving, bythe server, personal data associated with the wearer of the device; andorganizing, by the server, the first physiological data, based, at leastin part, on the tag and the personal data.
 17. The method of claim 16,wherein the personal data comprises one or more of: (a) a height of thewearer of the device, (b) a weight of the wearer of the device, (c) anage of the wearer of the device; (d) a gender of the wearer of thedevice; (e) a race of the wearer of the device; (f) a medical history ofthe wearer of the device; or (g) an occupation of the wearer of thedevice.
 18. The method of claim 1, wherein determining the learned rulecomprises: identifying one or more patterns of the first physiologicaldata and the second physiological data; determining that the thirdphysiological data has the identified one or more patterns; anddetermining the learned rule based on the third physiological datahaving the identified one or more patterns.
 19. The method of claim 1,wherein the tag comprises a label or annotation, and wherein associatingall or part of the first physiological data with the tag comprisesstoring the first physiological data in a database and applying thelabel or annotation to all or part of the stored first physiologicaldata.
 20. A method, comprising: receiving, by a server, firstphysiological data detected by a wearable device; receiving, by theserver, an input from a wearer of the wearable device; associating, bythe server, all or part of the first physiological data with a tagbased, at least in part, on the input from the wearer of the wearabledevice; establishing, by the server, one or more groups based, at leastin part, on the tag; storing, by the server, the first physiologicaldata associated with the tag in a database to indicate that all or partof the first physiological data associated with the tag is a member ofthe one or more groups; receiving, by the server from one or more otherwearable devices, second physiological data related to one or morewearers of the one or more other wearable devices, wherein the secondphysiological data is associated with the tag; storing, by the server,the second physiological data associated with the tag in the database toindicate that all or part of the second physiological data associatedwith the tag is a member of the one or more groups; receiving, by theserver from the wearable device, third physiological data detected bythe wearable device; determining, by the server, a learned rule based,at least in part, on (i) a comparison of at least some of the thirdphysiological data and at least some of the first physiological dataassociated with the tag and (ii) a comparison of at least some of thethird physiological data and at least some of the second physiologicaldata associated with the tag; associating, by the server, at least someof the third physiological data with the tag based on the learned rule;and storing, by the server, the third physiological data associated withthe tag in the database to indicate that all or part of the thirdphysiological data associated with the tag is a member of the one ormore groups.
 21. The method of claim 20, wherein the group is furtherbased, at least in part, on personal data of the wearer of the device.22. The method of claim 20, further comprising: receiving, by theserver, motion data associated with the wearer of the wearable device,wherein the motion data is detected synchronously with the firstphysiological data; receiving, by the server, time-synchronization datathat indicates a timing relationship between the motion data and thefirst physiological data; associating, by the server, all or part of themotion data with the tag based, at least in part, on thetime-synchronization data; and storing, by the server, the motion datain the database to indicate that all or part of the motion data is amember of the one or more groups.
 23. The method of claim 20, furthercomprising: receiving, by the server, contextual data associated withthe wearer of the device, wherein the contextual data is detectedsynchronously with the first physiological data; receiving, by theserver, time-synchronization data that indicates a timing relationshipbetween the contextual data and the first physiological data;associating, by the server, all or part of the contextual data with thetag based, at least in part, on the time-synchronization data; andstoring, by the server, the contextual data in the database to indicatethat all or part of the contextual data is a member of the one or moregroups.
 24. A method, comprising: receiving, by a server, firstphysiological data from a wearable device; receiving, by the server, aninput from a wearer of the wearable device; applying, by the server, atag to all or a portion of the first physiological data based on theinput from the wearer of the wearable device; receiving, by the serverfrom one or more other wearable devices, second physiological datarelated to one or more wearers of the one or more other wearabledevices, wherein the tag is applied to the second physiological data;receiving, by the server, third physiological data from the wearabledevice; determining, by the server, a learned rule based, at least inpart, on (i) a comparison between at least some of the thirdphysiological data and at least some of the first physiological datahaving the applied tag and (ii) a comparison of at least some of thethird physiological data and at least some of the second physiologicaldata having the applied tag; and determining, by the server, whether toapply the tag to all or a portion of the third physiological data based,at least in part, on the learned rule.
 25. The method of claim 24,wherein the tag is generated based, at least in part, on the input fromthe wearer.
 26. The method of claim 24, wherein the input is a state ofthe wearer, and wherein the state comprises one or more of: (a) a typeof activity engaged in by the wearer, (b) a task performed by thewearer, (c) a health state of the wearer, (d) a physical state of thewearer, (e) a mental state of the wearer, or (f) a mood of the wearer.